Vegetation biomass estimates across drylands at regional scales are critical for ecological modeling, yet the low-lying and sparse plant communities characterizing these ecosystems are challenging to accurately quantify and measure their variability using spectral-based aerial and satellite remote sensing. To overcome these challenges, multi-scale data including field-measured biomass, terrestrial laser scanning (TLS) and airborne laser scanning (ALS) data, were combined in a hierarchical modeling framework. Data derived at each scale were used to validate an increasingly broader index of sagebrush (Artemisia tridentata) aboveground biomass. First, two automatic crown delineation methods were used to delineate individual shrubs across the TLS plots. Second, three models to derive shrub volumes were utilized with TLS data and regressed against destructively-sampled individual shrub biomass measurements. Third, TLS-derived biomass estimates at 5 m were used to calibrate a biomass prediction model with a linear regression of ALS-derived percent vegetation cover (adjusted R2 = 0.87, p < 0.001, RMSE = 3.59 kg). The ALS prediction model was applied to the study watershed and evaluated with independent TLS plots (adjusted R2 = 0.55, RMSE = 4.01 kg, normalized RMSE = 35%). The biomass estimates at the scale of 5 m is sufficient for capturing the variability of biomass needed to initialize models to estimate ecosystem fluxes, and the contiguous estimates across the watershed support analyzing patterns and connectivity of these dynamics. Our model is currently optimized for the sagebrush-steppe environment at the watershed scale and may be readily applied to other shrub-dominated drylands, and especially the Great Basin, U.S., which extends across five western states. Improved derived metrics from ALS data and collection of additional TLS data to refine the relationship between TLS-derived biomass estimates and ALS-derived models of vegetation structure, will strengthen the predictive power of our model and extend its range to similar shrubland ecosystems.
Reynolds Creek, LiDAR, TLS, sagebrush, biomass
XML is in ISO-19115 geographic metadata format, compatible with ESRI Geoportal Server.
Citation for This Dataset
Li, Aihua; Glenn, Nancy F.; Olsoy, Peter J.; Mitchell, Jessica J.; and Shrestha, Rupesh. (2015). Data for Aboveground Biomass Estimates of Sagebrush Using Terrestrial and Airborne LiDAR Data in a Dryland Ecosystem [Data set]. Retrieved from http://dx.doi.org/10.18122/B2WC74
Citation for This Webpage
Li, Aihua; Glenn, Nancy F; Olsoy, Peter J; Mitchell, Jessica J; Shrestha, Rupesh (2012). "CZO Dataset: Reynolds Creek - Land Cover, LiDAR, Vegetation (2012) - Biomass Estimate of Sagebrush." Retrieved 18 Apr 2019, from http://criticalzone.org/national/data/dataset/6273/
Data Use Policy
1. Use our data freely. All CZO Data Products* except those labelled Private** are released to the public and may be freely copied, distributed, edited, remixed, and built upon under the condition that you give acknowledgement as described below. Non-CZO data products — like those produced by USGS or NOAA — have their own use policies, which should be followed.
2. Give proper citation and acknowledgement. Publications, models and data products that make use of these datasets must include proper citation and acknowledgement. Most importantly, provide a citation in a similar way as a journal article (i.e. author, title, year of publication, name of CZO “publisher”, edition or version, and URL or DOI access information. See http://www.datacite.org/whycitedata). Also include at least a brief acknowledgement such as: “Data were provided by the NSF-supported Southern Sierra Critical Zone Observatory” (replace with the appropriate observatory name).
3. Let us know how you will use the data. The dataset creators would appreciate hearing of any plans to use the dataset. Consider consultation or collaboration with dataset creators.
*CZO Data Products. Defined as a data collected with any monetary or logistical support from a CZO.
**Private. Most private data will be released to the public within 1-2 years, with some exceptionally challenging datasets up to 4 years. To inquire about potential earlier use, please contact us.
Data Sharing Policy
All CZO investigators and collaborators who receive material or logistical support from a CZO agree to:
1. Share data privately within 1 year. CZO investigators and collaborators agree to provide CZO Data Products* — including data files and metadata for raw, quality controlled and/or derived data — to CZO data managers within one year of collection of samples, in situ or experimental data. By default, data values will be held in a Private CZO Repository**, but metadata will be made public and will provide full attribution to the Dataset Creators†.
2. Release data to public within 2 years. CZO Dataset Creators will be encouraged after one year to release data for public access. Dataset Creators may chose to publish or release data sooner.
3. Request, in writing, data privacy up to 4 years. CZO PIs will review short written applications to extend data privacy beyond 2 years and up to 4 years from time of collection. Extensions beyond 3 years should not be the norm, and will be granted only for compelling cases.
4. Consult with creators of private CZO datasets prior to use. In order to enable the collaborative vision of the CZO program, data in private CZO repositories will be available to other investigators and collaborators within that CZO. Releasing or publishing any derivative of such private data without explicit consent from the dataset creators will be considered a serious scientific ethics violation.
* CZO Data Products. Defined as data collected with any monetary or logistical support from a CZO. Logistical support includes the use of any CZO sensors, sampling infrastructure, equipment, vehicles, or labor from a supported investigator, student or staff person. CZO Data Products can acknowledge multiple additional sources of support.
** Private CZO Repository. Defined as a password-protected directory on each CZO’s data server. Files will be accessible by all investigators and collaborators within the given CZO and logins will be maintained by that local CZO’s data managers. Although data values will not be accessible by the public or ingested into any central data system (i.e. CUAHSI HIS), metadata will be fully discoverable by the public. This provides the dual benefit of giving attribution and credit to dataset creators and the CZO in general, while maintaining protection of intellectual property while publications are pending.
† Dataset Creators. Defined as the people who are responsible for designing, collecting, analyzing and providing quality assurance for a dataset. The creators of a dataset are analogous to the authors of a publication, and datasets should be cited in an analogous manner following the emerging international guidelines described at http://www.datacite.org/whycitedata.